Method for predicting the quality of a product

ABSTRACT

A method is provided for predicting a quality characteristic of a product to be manufactured. The method may integrate one or more of feature and tolerance information associated with the product, manufacturing characteristic information associated with the manufacture of the product, measurement capability characteristic information associated with the manufacture of the product, assembly characteristic information associated with an assembly of the product, and desired quality characteristic information associated with the product. Based on the integrated information, the quality characteristic of the product may be predicted.

This is a continuation of application Ser. No. 10/320,802, filed Dec.17, 2002 now U.S. Pat. No. 6,823,287.

TECHNICAL FIELD

The present invention relates generally to product manufacturing and,more particularly, to a method for monitoring and analyzing the qualityof a product.

BACKGROUND

Products are often manufactured based on physical and/or functionalcharacteristics of one or more components included in the product. Forexample, a fuel injector must be manufactured with predetermineddimensions in order to be compatible with a host machine. Further, thefuel injector must be manufactured so that it performs in a mannerrequired for proper operation of the machine. Also, a product may becomposed of a number of components that each must meet physical andfunctional characteristics to ensure the product meets specifiedcriteria. Accordingly, high quality, precise manufacturing is requiredto ensure a product will operate as expected and within acceptabletolerances.

To ensure a product is manufactured according to its specified criteria,manufacturers may perform quality control checks during and following amanufacturing process. For example, a manufacturer may determine thequality of one or more components that are included in a product priorto assembling them into the product. The quality determination for eachcomponent may be based on one or more parameters that affect theassembly or the function of the product. Although measuring the qualityof components during an assembly operation may provide some insight onthe quality of a product's components, the piece meal analysis of theindividual components does not provide a reliable analysis of theproduct's overall quality.

To address these reliability problems, manufacturers may perform a FirstTest Pass (FTP) analysis that involves testing a manufactured product ina performance tester device to determine whether the physical and/orfunctional characteristics of a product's components are acceptable(e.g., within predetermined tolerances). Along with testing the qualityof the manufacturing of a product, the FTP analysis may also provide acomprehensive test of manufacturing capability, quality controlstrategy, production environment, etc., since the criteria of a product(e.g., physical and functional) are being tested on the product'scomponents as a whole. An FTP analysis produces a value called an FTPpass rate, which reflects the percentage of components tested that areacceptable. Most manufacturers aim for high FTP pass rates, such as 95%,because the pass rate directly affects profits. That is, if a product'sFTP pass rate is too low, the product may have to be redesigned and/orremanufactured.

The FTP pass rate may be based on multiple factors associated with amanufacturing process such as: manufacturing capability, assemblyprocedure, measurement capability, sensitivity features, and FTP testcriteria. Manufacturing capability reflects the ability of amanufacturer to meet predetermined design requirements, such as nominalmeasurements and tolerances. To minimize functional and/or physicalvariations in manufactured products, a manufacturer must also be able tominimize the ratio of a manufacturing range of specifications over atolerance range of specifications, which is known as the Cpk factor.Assembly procedures also affect variation between products. For example,improper assembly procedures may increase assembly error, which in turnwill lower the FTP pass rate for a corresponding product. Measurementcapability reflects the ability to measure various types ofcharacteristics of product components that are included in the assemblyprocedure. Errors in measurement may significantly affect the variationbetween product because the assembly process may be unnecessarilyadjusted to compensate for incorrect measured component characteristics.Sensitivity features denote one or more features that have significanteffect on the performance of a product. Finally, FTP test criteria areassociated with standards for quality control based on customersrequirements.

One conventional method for determining the quality of a assemblyprocess is described in U.S. Pat. No. 5,452,218 issued on Sep. 19, 1995to Tucker et al. The method described in the '218 patent includescollecting manufacturing capability data stored in a database to model aproduct to produce a measure of quality for the product. Although themethod in the '218 patent provides quality information for a product,the method does not consider a combination of information such asmanufacturing capability, assembly procedure, measurement capability,sensitivity features, and desired quality characteristics associatedwith the product. Accordingly, the measure of quality for the productare based on limited information leaving a product open to additionaldefect producing operations that are not accounted for by the method.

There is currently no method for analyzing and monitoring pass ratesbased on the relationship between the above mentioned factors. Thepresent invention is directed to solving one or more of the problems setforth above.

SUMMARY OF THE INVENTION

A method is provided for predicting a quality characteristic of aproduct to be manufactured. The method may include identifying a featureand an associated tolerance associated with the product. Further themethod may include determining a manufacturing characteristic associatedwith a manufacture of the product, determining a measurementcharacteristic associated with the manufacture of the product,determining an assembly characteristic associated with the assembly ofthe product, and determining a desired quality characteristic associatedwith the product. Based on the determined characteristics and identifiedfeature and tolerance, the method may predict the quality characteristicof the product.

Further, a system is provided for predicting a quality characteristic ofa product is provided. The system may comprise a memory includinginstructions for identifying a feature and associated toleranceassociated with the product. Further, the memory may includeinstructions for determining a manufacturing characteristic associatedwith a manufacture process of the product, instructions for determininga measurement characteristic associated with the manufacture of theproduct, instructions for determining an assembly characteristicassociated with the assembly of the product, and instructions fordetermining a desired quality characteristic associated with theproduct. Also, the memory may include instructions for predicting thequality characteristic of the product based on the determinedcharacteristics and identified feature and associated tolerance. Inaddition to the memory, the system may also include a processorconfigured to execute each of the instructions included in the memory.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate exemplary embodiments of theinvention and together with the description, serve to explain theprinciples of the invention. In the drawings:

FIG. 1 illustrates an exemplary system that may be configured to performcertain functions consistent with embodiments of the present invention;

FIG. 2 illustrates a flowchart of an exemplary FTP analysis methodconsistent with embodiments of the present invention;

FIG. 3 illustrates an exemplary table of features consistent withembodiments of the present invention;

FIG. 4 illustrates an exemplary table of features and measuringcapabilities consistent with embodiments of the present invention;

FIG. 5 illustrates an exemplary table of sub-assembly category partsconsistent with embodiments of the present invention;

FIG. 6 illustrates an exemplary table of FTP reference test data and FTPtest criteria consistent with embodiments of the present invention;

FIG. 7 illustrates an exemplary table of statistical productioninformation consistent with embodiments of the present invention;

FIG. 8 illustrates a flowchart of an exemplary gain factor processconsistent with embodiments of the present invention;

FIG. 9 illustrates an exemplary table of performance variationinformation consistent with embodiments of the present invention; and

FIG. 10 illustrates an exemplary diagram of a distribution curve withina criteria window consistent with embodiments of the present invention.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary system 100 in which embodiments of thepresent invention may be implemented. As shown in FIG. 1, system 100 mayinclude a plurality of modules that perform various functions. In oneembodiment of the present invention, system 100 includes an analysissystem 105 and external entities 140-1 to 140-N.

Analysis system 105 is a computing system that is configured to receiveinformation from entities 140-1 to 140-N to perform analysis andprediction functions consistent with embodiments of the invention, suchas FTP analysis functions. Analysis system 105 may include processor110, memory 120, and interface device 130. Processor 110 may be one ormore processor devices, such as a microprocessor, laptop computer,desktop computer, workstation, mainframe, etc. that execute programinstructions to perform various functions. Memory 120 may be one or morestorage devices that maintain data (e.g., instructions, softwareapplications, etc.) used by processor 110. In one embodiment of thepresent invention, memory 120 includes data and software that isaccessed and executed by processor 110 to analyze a qualitycharacteristic of a product before its manufacture. A product, as usedherein, may represent any type of physical good that is designed,developed, manufactured, assembled, and/or delivered by a source, suchas, for example, a manufacturer. A product may be composed of one ormore components that collectively form the product. Interface 130 may beone or more interface devices configured to facilitate the exchange ofdata between system 105 and external sources, such as entities 140-1 to140-N.

Entities 140-1 to 140-N may be one or more sources of information thatare used by system 105 to perform analysis functions. In one embodiment,entities 140-1 to 140-N are associated with one or more departments of amanufacturer of a product. For example, entity 140-1 may be associatedwith an engineering department that provides design and tolerance data.An entity 140-2 (not shown) may be associated with a manufacturingdepartment that provides manufacturing characteristics, such asstatistical distribution information, which may include Cpk values forthe manufactured product. Further, entity 140-N may be associated withan assembly department that provides information corresponding to theassembly of the product during its manufacture.

Although FIG. 1 shows the configuration of elements 105 and 140-1 to140-N as separate elements, one skilled in the art would realize thatsystem 100 may be implemented in a number of different configurationswithout departing from the scope of the present invention. For example,system 105 and entities 140-1 to 140-N may be combined into a singlemodule that includes software, hardware, and/or a combination of both.Alternatively, system 100 may be configured as a distributed system,with modules 105 and 140-1 to 140-N distributed in remote locations andinterconnected by communication paths, such as Local Area Networks(LANs), Wide Area Networks (WANs), and any other type of network thatmay facilitate communications and the exchange of information betweenthe elements in FIG. 1 and/or any other elements that may be implementedby system 100.

In one embodiment of the invention, exemplary system 100 may beconfigured to predict a quality characteristic, such as a FTP rate, forone or more products. FIG. 2 shows a flowchart of an exemplary analysismethod that may be performed by system 100. Although the analysis methodshown in FIG. 2 will be described with respect to one type of product(e.g., fuel injector) built by a manufacturer, one skilled in the artwould appreciate that the following description may apply to any type ofproduct.

The analysis method may begin by system 105 determining features andtolerances for certain components (e.g., parts) of the product beinganalyzed, such as an exemplary fuel injector (step 200). For exemplarypurposes, the components associated with these features are labelednon-category parts. In one embodiment, all, or most of the features andtolerances associated with the components are identified. In analternative embodiment, a feature may be considered to be a physicaland/or functional characteristic of a component (e.g., non-categorypart) that significantly affects the performance of a product. Becausethe performance of a product is related to a number of physical and/orfunctional features (e.g., dimensions, spring rate, etc.), system 105may facilitate various processes that enable the features of a productto be identified. To facilitate these processes, system 105 may retrieveproduct data from one or more entities 140-1 to 140-N, such as productspecifications (e.g., dimensions, threshold limits, functionalcharacteristics, etc.) and statistical data associated with thesespecifications.

In one embodiment, system 100 may identify features of a product byperforming a sensitivity process that determines the sensitivity of theproduct's performance to its product specifications. System 100 mayperform the sensitivity process via empirical methods or simulationmethods. For example, in an empirical analysis, a set of samples of aproduct are tested with varying test parameters (e.g., variousdimensions, operating conditions, etc.). Simulation methods usemathematical models to simulate a product with varying test parameters.By varying the parameters in the model, system 100 may determinerelationships between product specifications and performance. Forexample, varying the size of a nozzle in a fuel injector may adjust theperformance of the simulated injector. In one embodiment, one or moreentities 140-1 to 140-N may provide the results of a sensitivity processto system 105. Alternatively, system may perform the sensitivity processbased on product analysis information received from one or more entities140-1 to 140-N. Based on the results of the sensitivity analysis, system105 may identify one or more features of a product to perform theanalysis.

For example, system 105 may determine through sensitivity analysis thatslight changes in the nozzle size of the exemplary fuel injector resultin significant changes in performance of the injector. Accordingly,system 105 may identify nozzle size as a feature of the fuel injector tobe analyzed. On the other hand, if changes in another component of thefuel injector result in small or insignificant changes in performance,system 105 may determine that the other component does not impact thequality characteristic enough to analyze it. Essentially, thesensitivity analysis determines the impact on the performance of aproduct when one or more components and/or functional parameters varies,even slightly, from nominal values. Sensitivity analysis determineswhich of these components and/or parameters contribute the most to theperformance of the product. In one embodiment, features may beidentified for analysis based on the sensitivity analysis.

System 105 may identify the features to analyze automatically, manually,or a combination of both, based on the results of the sensitivityanalysis. For example, system 105 may provide sensitivity results to auser operating system 105. The user may identify the features to analyzeof a product based on the results. The identified features may then bestored in memory 120 for use during the analysis method.

In another embodiment, system 105 may also identify one or moretolerances for each of the identified features. A tolerance indicates arange of values at which a variance for the feature is acceptable. Forexample, a tolerance range may be based on a nominal value plus or minusa tolerance bound (e.g., 50 mm±0.5 mm). The nominal value may be anactual measured mean value of a feature or may be a value calculated bysystem 105 and/or a user. Further, a tolerance may be based on amanufacturing metric, such as Cpk, where the tolerance range isexpressed in standard deviations. FIG. 3 shows a table 300 listing thefeatures 310 of a plurality of non-category parts 320 for a fuelinjector. As shown, the features 310 of the exemplary injector mayinclude a nominal value 330 reflecting a designed value determined bysystem 105. Mean value 340 reflects an actual measured value based onthe analysis of a number of corresponding samples of the respectivenon-category part. Variations between the measured mean values 340 andthe designed nominal values 330 reflect a variance in the actualcharacteristics of the corresponding non-category part. This variancemay result in a shift in the mean performance of the product thatincludes these varying non-category parts.

Table 300 also shows for each non-category part a corresponding designedtolerance window consisting of an Upper Tolerance (UT) bound 350 and aLower Tolerance (LT) bound 360. The designed nominal value and tolerancewindow may be reflected in the following relationship:Nominal−LT Bound≦Non-category part dimension≦Nominal+UT bound

Cpk 370 is a value reflecting the manufacturing capability for thecorresponding non-category part and may be defined by the followingrelationship:Cpk=(UTB+LTB)/6σ, where

σ or is the standard deviation of the non-category part dimension.Accordingly, the higher the Cpk value, the higher the capability of theprocess used in manufacturing the non-category part.

In addition to determining features and tolerances, system 105 may alsoidentify assembly features and category parts (step 210). Category partsare components of a product that when assembled together provide anassembly feature. For example, following the exemplary fuel injectorproduct example, air gap is an assembly feature because it is not anactual component that is assembled to the product. To set air gapaccurately, a number of different category parts are involved, such asan armature, solenoid spacer, seated pin, upper and lower seat, andseated pin travel spacer. In one embodiment, system 105 may select aportion (e.g., one) of the category parts for an assembly feature. Forexample, for air gap, system 105 may select solenoid spacer as acategory part. Category parts may be treated the same as features, ormay be treated differently. For example, a distribution ofcharacteristics for each category part may be assumed to be uniform,thus the standard deviation may be calculated rather than measured.

System 105 may also determine manufacturing characteristics associatedwith the components of the product under analysis (step 220). Themanufacturing characteristics may be indicative of how well thecomponent may be manufactured. In one embodiment, a potentialmanufacturer of a component (e.g., category and non-category parts) mayprovide input to system 105 reflecting a tolerance range for theirparticular component and/or how much of the tolerance range they areusing. A manufacturer will typically determine a component's tolerancerange by actually testing a sample set of the components. As discussedabove, the Cpk factor is one designation of a manufacturer'scapabilities, and may be described as the ratio of the manufacturer'srange over the tolerance range. For example, a certain component mayhave a requirement of a diameter of 4 mm plus/minus 0.1 mm. Amanufacturer may test a number of these produced components to determineactual measurements for the diameter. Any variances between the requireddiameter and actual measured diameter may be collected and a statisticcalculated, such as a normal distribution based on the actualmeasurements. A higher Cpk value indicates that the manufacturer isworking within a smaller range of the variance inside the permittedtolerances, i.e., the parts have less variation among themselves. In analternative embodiment, rather than receiving data from a manufacturerbased on actual tests, a Cpk or other value may be determined from asimulation performed by system 105 or another entity, such as entity140-1.

Many measurements may be used in the assembly process of a product totest the quality of components and the assembly process itself.Measurements may be affected not only by the type of device used tomeasure (e.g., gauge), but also by environmental conditions (e.g.,temperature and/or humidity), calibration of the measuring device, andhuman error. Accordingly, system 105 is also able to determine themeasurement characteristics associated with the manufacture of a product(step 230). In one embodiment, measurements may include one or moregauge characteristics. For example, gauge characteristics may includegauge repeatability and reproducibility (GR&R) and gauge offset. GR&R isthe accuracy of a measurement device to produce consistent and accuratemeasurement values. GR&R also reflects the effect of human error andenvironment change on accurate measurements. Gauge offset reflects thecalibration status for the measurement device for its zero referenceposition. FIG. 4 shows a table 400 listing a number of features used inthe assembly of the exemplary fuel injector product. As shown, table 400includes exemplary parameters 405 associated with category parts 410,including GR&R percentage values 420 and gauge offset values 430.

Because measurement errors usually have a normal distribution, system105 may use normal distribution calculations to determine thestatistical characteristics for these types of errors. In oneembodiment, GR&R is defined based on the measurement range of themeasurement device used to measure a feature or category part andrepresented as a percentage of the range. System 105 may calculate themeasurement error from the measurement range and the gauge GR&R based onthe following relationship:σ=Δ(GR&R)/C, where

Δ is the measurement range of the measuring device, σ is the standarddeviation of the measurement error, and C is a predetermined constant.

System 105 may measure parameters directly or calculate their valuesfrom one or more directly measured parameters. For example, system 105may use the following error transfer function to determine an error ofan indirectly measured parameter:Y=F(Xi)σ²=Σ((δF/δXi)σ_(Xi))²

where Y is an indirectly measured parameter, F(Xi) is the function of Yrelated to the directly measured parameter Xi, σ_(Y) is the standarddeviation of Y, and σ_(X) is the standard deviation of Xi. To illustratethis embodiment further, consider a spring load component for theexemplary fuel injector where the spring assembly length is directlymeasured. Accordingly, the error in the spring load is calculated fromthe relationship between the spring load and its assembly length. Forexample,Fs=KXσ² _(F)=((δF _(s) /δX)σ_(X))²=(Kσ _(X))²

Generally, a product is built in accordance with a designed assemblyprocedure that is associated with the components of the product. Becausethe assembly procedure has an impact on the overall performance of theproduct, variations between components used to repeatedly assemble anumber of similar products are taken into consideration by system 105.For example, if the values shift because the gauges used during assemblyare improperly calibrated, the features associated with the assembly maybe outside of the acceptable tolerances. Accordingly, in step 240,system determines assembly characteristic information associated withthe manufacture of the product under analysis. In one embodiment of theanalysis method, the assembly process includes two steps, thesub-assembly of components (using category parts) and final assembly(including all features).

To model the sub-assembly process, system 105 may receive designednominal and tolerance window (e.g., upper and lower bounds) informationfor one or more category parts that are used to assemble the componentsof the product. System 105 may receive this information from one or moreentities 140-1 to 140-N, such as an assembly department of themanufacturer of the product. Based on information relating to theassembly procedure of the components using the category parts, system105 calculates an actual mean and variation standard deviation value foreach category part. Because the features of the category parts includedin the sub-assembly procedure may have a normal distribution, the meanvalue may shift from the designed nominal value if a measuring device(e.g., a gauge) involved in taking measurements during the sub-assemblyprocedure has offsets due to mis-calibration. Accordingly, if a featureof a category part is related to a measured variable, system 105determines the mean value for the feature based on the followingrelationship:

${{\Delta\; Y} = {\sum\limits_{i}\left( {\left( {\delta\;{F/\delta}\; X} \right)\Delta\;{Xi}} \right)}},{where}$

ΔY is the mean value shift from the nominal designed value, ΔX is thegauge offset for variable X, and F is the function between thesub-assembly feature and measurement X.

System 105 may determine the function of Y and X based on the assemblyprocedure. To illustrate this embodiment, consider an exemplary aassembly procedure for exemplary fuel injector product. This exemplaryassembly procedure may begin with measuring spool travel associated witha spool valve with respect to a reference body plate thickness to obtaina spool travel measurement X₁. Next, the reference body plate thicknessfor setting the measured spool travel is measured, which is labeled X₂.The difference between the measured spool travel and the designednominal is then calculated using the relationship X₁−X_(Nominal). Therequired body plate thickness for setting the spool travel to nominal isdetermined using the relationship:Y=X ₂+(X ₁ +X _(Nominal)).

The category of the body plate based on Y is selected, and a spool valveis then assembled. Based on the above exemplary procedure, system 105may determine the shift of the mean value for the spool travel from itsnominal value if the measurement gauge has an offset of:ΔY=ΔX ₁ +ΔX ₂.

The assembled mean value for the spool travel may then be determined bysystem 105 based on the relationship:X=X _(Nominal) +ΔY.

The variation transfer through the assembly of the spool travel isdetermined based on the following relationship:σ² _(Y)=Σ((δF _(s) /δXi)σ_(Xi))².

Based on the spool assembly procedure, system 105 may determine thatthere are three variations involved in the above exemplary sub-assemblyprocess. These are the measurement variance of the body plate and spooltravel and the variance of the body plate as a category part.Accordingly, system 105 may determine the variance of spool assemblybased on the following relationship:σ_(Y)=(σ² _(Body.m)+σ² _(Spool.m)+σ² _(Body.C))^(1/2), where

σ² _(Body.m) is the measurement variance of the body plate, σ²_(Spool.m) is the measurement variance of the spool travel, and σ²_(Body.C) is the variance of the body plate as a category part.

System 105 may also determine the standard deviation of the sub-assemblyfor each category part in a manner similar to the assembly proceduredescribed above. FIG. 5 shows a table 500 listing exemplary features 510for exemplary category parts involved in an exemplary sub-assemblyprocess and their corresponding calculated (e.g., mean and standarddeviation) and designed (e.g., nominal and tolerance window) values, 520and 530, respectively.

Returning back to FIG. 2, system 105 may also determine a desiredquality characteristic. For example, a desired quality characteristicmay be FTP test criteria that is determined during the analysis method(step 250). The FTP test criteria may be based on the requirements orguidelines provided by, but not limited to, one or more customers ordesign groups, and may include a correlation between product performanceand test criteria. System 105 may receive from an entity (e.g., entity140-1) test criteria that includes values for one or more selected testpoints associated with the product under analysis. For example, in theabove exemplary fuel injector product, a rated condition associated withthe injector may be used as an FTP test criterion, such as rateddelivery, timing, etc. To determine an error of a correlation betweenactual product performance and the FTP test criteria, system 105 maytest a set of reference products to obtain performance informationrelated to the selected FTP test criteria. For example, a number of theexemplary fuel injector products may be tested in a testing facility(e.g., test bench) to obtain reference data for the selected FTP testcriteria (e.g., rated delivery, timing, etc.). System 105 may receivethe results of these reference tests to determine the mean value ofperformance of the reference products. Further, system 105 may determinethe standard deviation for each reference product's corresponding testcriteria. The standard deviation represents the variation betweenreference values between tests (e.g., daily, weekly, etc.). Accordingly,large standard deviation values may indicate to system 105 that there issome instability in the FTP testing process.

As indicated above, system 105 may determine the test criteria based onone or more customer requirements. These requirements may include anominal value for the selected FTP test criteria. System 105 determineswhether there is proper correlation between the FTP test criteria andthe product performance by comparing the determined mean values for thereference products and their corresponding nominal values associatedwith the FTP test criteria.

FIG. 6 shows an FTP reference test table 610 and an FTP test criteriatable 620 associated with an exemplary fuel injector. As shown in thefigure, the correlation between the mean value 630 and a nominal value640 for each test criteria 605 determines whether the product (e.g.,fuel injector) will have a FTP pass rate that is accurate. System 105may determine, based on the correlation between these two values (e.g.,mean and nominal values), whether the performance of the product isbeing evaluated correctly.

As described, steps 210-250 of the analysis process provide acorrelation between information associated with the manufacturing,assembly, testing, and desired quality characteristics for a product.Based on this assembled information, system 105 may perform an FTPanalysis and prediction process (step 260).

In one embodiment, system 105 may predict the performance variation ofthe assembly and manufacturing of a product based on a relationshipbetween the features and the performance of the product. Accordingly,system 105 performs a sensitivity analysis to develop the relationship.To perform a sensitivity analysis, system 105 determines thecharacteristics associated with the product under analysis and developsa relationship function between the features identified in step 200 andthe performance of the product. For example, a fuel injector product mayinclude performance aspects of fuel delivery (Q) and injection timing(T). Accordingly, system 105 may develop the relationship between theinjector's features and the performance based on the followingfunctions:Q=Q(Xi)T=T(Xi).

System 105 may be configured to assume that the variable X changes in asmall range of values. Therefore, the relationship between theperformance variation with the variation of X may be determined as:

${\Delta\; Q} = {\sum\limits_{i}\left( {{\left( {\delta\;{Q/\delta}\;{Xi}} \right)\Delta\;{Xi}},{{{and}\Delta\; T} = {\sum\limits_{i}\left( {\left( {\delta\;{T/\delta}\;{Xi}} \right)\Delta\;{{Xi}.}} \right.}}} \right.}$

Based on the above relationships, system 105 may determine the gainfactors (G) of variable X for the delivery and timing as:G _(Q.X)=(δQ/δXi), andG _(T.X)=(δT/δXi).

The gain factors G for each performance aspect of the product may bedetermined from simulation or actual test processes. To determine thecorrelation between the performance aspects and features of the product,system 105 may determine the mean value and variation range based on themanufacturing and assembly information determined in steps 220 and 240of the FTP analysis method. Therefore, for category parts of theproduct, the upper and lower bound of the variation range may bedetermined by system 105 based on the following relationships:UBound_(—)3σ=UT_Bound+(Nominal−Mean)/Cpk, andLBound_(—)3σ=LT_Bound−(Nominal−Mean)/Cpk.

The above relationships take into account the effect of the shift of themean value on the statistical distribution of the feature associatedwith the category part. System 105 may determine the upper and lowerbound of the variation range for sub-assembly features using a differentrelationship, defined as:UBound_(—)3σ=3σ, andLBound_(—)3σ=3σ.

FIG. 7 shows a table 700 listing exemplary statistical productioninformation 710 and gain factors 720, 730 for the delivery and timingperformance aspects of an exemplary fuel injector product. As shown, thegain factors 720 and 730 include values for two test points, rated 740and idle 750 conditions. A product under analysis may include additionalor fewer test points associated with the performance aspects of theproduct. Accordingly, system 105 determines the gain factors for eachtest point associated with the product's performance.

To obtain accurate gain factors, system 105 correlates the simulationmodel of the product to reflect variations of the product. In oneembodiment, system 105 correlates the statistical distribution ofperformance of the product with the statistical distribution of itsfeatures.

FIG. 8 shows a gain factor process that may be performed to obtainaccurate gain factors for a product. As shown, the gain factor processbegins with a manufacturer building R units (e.g., 31 units) of aproduct and providing complete documentation of its features (Step 810).Next, FTP tests in an FTP test environment (e.g., FTP test bench) areperformed on the R units to obtain performance information associatedwith one or more FTP test points for the product (step 820). Thestatistical distributions of the features for building the R units isthen determined (step 830). Also, the statistical distribution of theperformance of the R units at the FTP test points is determined (step840). Manufacturing and assembly information associated with the R unitsis used by system 105 to conduct a simulation analysis of theperformance at the FTP test points (step 850). Based on results from thesimulation analysis, system 105 may determine the statisticaldistribution of performance for the product (step 860). System 105 maycompare the statistical characteristics of the simulation analysisresults with the FTP test results (step 870), and based on thecomparison, correlates the simulation model until it matches the FTPtest data within a predetermined threshold value (e.g., within a rangeof values) (step 880).

Once system 105 determines the gain factors for the product'sperformance aspects, the performance variation is determined using thefollowing relationships:ΔQi=G _(Q.Xi)ΔXi, andΔTi=G _(T.Xi)ΔXi.

To simplify the processing involved with determining the above exemplarygain factors, this embodiment may assume that there is no interactionbetween the factors. However, in instances where such a relationship mayexist, system 105 may be configured to determine the relationshipsbetween the features of the product. Further, the features may beindependent of each other. Accordingly, their variation may beconsidered small enough that system 105 may apply linear approximationtechniques to calculate the features' effect on the performance of theproduct. Alternatively, the features may have some dependency on eachother. Accordingly, system 105 may be configured to apply Design ofExperiment techniques (e.g., Taguchi method) to obtain a relationshipbetween the features. The variation of the features may be convertedinto a variation of performance of the product using performancevariation relationships ΔQi and ΔTi. FIG. 9 shows a table 900 listingexemplary performance variation information for the exemplary fuelinjector. As shown, table 900 includes two performance variation parts,the mean performance shift 910 reflecting the mean value shift from thenominal value of each feature and the upper and lower 3 standarddeviation 920 and 930, respectively, reflecting the variation of thefeature.

In one embodiment, system 105 may determine the mean performance shiftbased on the shift of the feature and the gain factor of the feature foreach of the performance aspects of the product. For example, consideringthe exemplary fuel injector product, the delivery and timing performanceaspects result in the following relationships:ΔQi=G_(Q.Xi)ΔXi, andΔTi=G_(T.Xi)ΔXi.

System 105 may then determine the overall shift mean performance basedon the following relationships:

${{\Delta\; Q} = {\sum\limits_{i}{\Delta\;{Qi}}}},{{{and}\mspace{14mu}\Delta\; T} = {\sum\limits_{i}{\Delta\;{{Ti}.}}}}$

Further, system 105 may determine the overall effects of the featuresfrom the following statistical summations:U3σ=[ΣMax(Upper_(—)3Sigma, Lower_(—)3Sigma)²]^(1/2)L3σ=[ΣMin(Upper_(—)3Sigma, Lower_(—)3Sigma)²]^(1/2)

System 105 may be configured to assume that the performance distributionin the above statistical summations are equivalent to a normaldistribution on both sides of the mean value. However, system 105 mayalso determine that the standard deviation on each side of the meanvalue may not be the same. That is, while 50% of a performancedistribution curve may be on the lower side of the mean performancevalue and the other 50% will be on the upper side of the meanperformance value, the standard deviation on each side of the meanperformance may be different.

In addition to FTP pass rate analysis, system 105 may predict the FTPpass rate by comparing the FTP criteria window with the productperformance distribution. FIG. 10 shows an exemplary product performancedistribution curve 1000 bounded by an FTP criteria window (e.g., LB andUB). As can be seen, only a portion 1010 of the distribution curve fallswithin the criteria window.

Based on the normalized distribution function, system 105 may determinea probability that the performance of the product falls into the FTPcriteria window using the following relationship:

FTP_(j) = ∫_(LB)^(UB)f(x)𝕕x, where

f(x) is the nominal distribution function, j is the FTP test criteria,LB is the normalized lower bound of the FTP window, and UB is thenormalized upper bound of the FTP window.

System 105 may determine the overall pass rate as the cumulative FTPpass rate determined above:

${FTP} = {\prod\limits_{j}{FTP}_{j}}$

System 105 may be configured to provide the predicted overall pass rateas a percentage value to an input/output device (not shown) for analysisby external entities, such as a user, or other computing systems (e.g.,entity 140-1).

INDUSTRIAL APPLICABILITY

The analysis method described above provides a way to consider and altermany of the factors contributing to the success (and failure) ofmanufactured components. In particular, the manufacturing and measuringcapabilities for a product are accounted for by an analysis system, asare the assembly procedures. Because the analysis system can determinewhich features are the most sensitive (or have the most significantimpact on the performance), these features can be more closelycontrolled and monitored. The desired quality characteristics, such asFTP test criteria, can be easily correlated with product performance.The analysis process described above permits the integration of some orall of the above described factors in a single method. As such, it ispossible to determine not only how likely it is for a product to fail(based on the FTP pass rate), but also where it is most likely to fail(e.g., certain components). The components can be identified andimprovement may be realized by changing the manufacturing, improving theassembly, and/or modifying the design, as relates to those components.The method also provides the basis for an economic model to assess costand benefits associated with various design and manufacturing scenarios.

Although the above discussion focused on an exemplary fuel injector, itshould be understood that any part, component, and/or product inmanufacture could be analyzed using embodiments of the presentinvention. Further, although certain steps and/or sub-steps of theanalysis process involve the intervention of a human operator, theseinterventions may also be performed automatically through simulation orthrough access of a knowledge management system known in the art.

It will be readily apparent to those skilled in this art that obviouschanges and modifications may be made to the described system andmethods, and all such changes and modifications are considered to fallwithin the scope of the appended claims. For example, system 105 may beconfigured to predict a change in the FTP pass rate based on a change inproduction times. For instance, failed units of a product may not bereworked, but rather taken apart. The disassembled components of theproduct may be reused to build new units. Accordingly, if failure ofunits is partially caused by defective components, these defectivecomponents may cause further damage when they are recycled into aproduction line. Accumulation of the defective parts through amanufacturing process life cycle may cause lower FTP pass rates.Accordingly, system 105 determines that the falling rate of the FTP passrate based on defective components is related to the number of defectivecomponents and the inventory of components in a production line. Basedon this correlation, system 105 may predict the variations of an FTPpass rate based on production time.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope of theinvention being indicated by the following claims and their equivalents.

1. A method of predicting a quality characteristic of a product to be manufactured by a manufacturer, comprising the steps of: integrating three or more of feature and tolerance information associated with the product, manufacturing characteristic information associated with a manufacture of the product, measurement characteristic information associated with the manufacture of the product, assembly characteristic information associated with an assembly of the product, and desired quality characteristic information associated with the product; predicting the quality characteristic of the product based on the integrated information; and providing the predicted quality characteristic of the product to the manufacturer.
 2. The method, as set forth in claim 1, wherein the step of integrating information further includes steps of: simulating the product to determine one or more features that affect a desired performance of the product; and identifying said feature and tolerance information associated with the product in response to said simulation.
 3. The method, as set forth in claim 2, wherein the step of integrating information further includes the step of determining a manufacturing characteristic associated with the manufacture of the product in response to said feature and tolerance information identification, thereby integrating said manufacturing characteristic and said feature and tolerance information.
 4. The method, as set forth in claim 3, wherein the step of integrating information further includes the step of determining an assembly characteristic associated with the assembly of the product in response to said manufacturing characteristic, thereby integrating said assembly characteristic and said manufacturing characteristic information.
 5. The method, as set forth in claim 2, wherein the step of integrating information further includes the step of determining an assembly characteristic associated with the assembly of the product in response to said feature and tolerance information identification, thereby integrating said assembly characteristic and said feature and tolerance information.
 6. The method of claim 2, further including: performing a sensitivity analysis to assess the impact of the one or more features on the desired performance.
 7. The method of claim 3, wherein the step of integrating information further includes the step of determining a manufacturing characteristic associated with a manufacture of the product, said manufacturing characteristic being indicative of a quality characteristic of a manufacturing process.
 8. The method of claim 1, wherein the step of integrating information further includes the step steps of determining a desired quality characteristic associated with the product, wherein said desired quality characteristic is a first test pass (FTP) criteria.
 9. The method of claim 3, wherein the step of determining a manufacturing characteristic includes: calculating a Cpk value reflecting a relationship between a manufacturing range of values and a tolerance range of values associated with the product.
 10. The method of claim 1, wherein the step of integrating information further includes the steps of: determining a gauge characteristic that is associated with a measuring device used during the manufacture of the product; determining said measurement characteristic in response to said gauge characteristic; and determining said assembly characteristic in response to said measurement characteristic, thereby integrating said measurement characteristic and said assembly characteristic.
 11. The method of claim 10, wherein the gauge characteristic is at least one of a repeatability and reproducibility value and a gauge offset value.
 12. The method of claim 1, wherein the desired quality characteristic reflects a performance requirement associated with the product.
 13. The method of claim 1, wherein the step of predicting the quality of the product includes the steps of: determining a gain factor for each of one or more performance aspects associated with the product based on a relationship between the feature and a desired performance of the product; determining performance variation using the established gain factors; and analyzing the effect of the feature on the performance of the product based on the determined performance variation.
 14. The method of claim 13, further including: determining a performance distribution based on the analyzed effect of the feature; and comparing the performance distribution to the desired quality characteristic.
 15. The method of claim 1, wherein the step of integrating said information includes the steps of: automatically identifying the feature and corresponding tolerance; automatically determining the manufacturing characteristic, determining the measurement characteristic; automatically determining the assembly characteristic; and automatically determining the desired quality characteristic in response to the feature and corresponding tolerance, said manufacturing characteristic and said assembly characteristic.
 16. The method of claim 2, further including: altering at least one of the identified feature and corresponding tolerance information, the determined manufacturing information or the measuring characteristic information, the determined assembly characteristic information, and the determined desired quality characteristic information; and predicting the quality of the product based on the at least one of altered identified feature and corresponding tolerance, the determined manufacturing and measuring characteristics, the determined assembly characteristic, or the determined desired quality characteristic.
 17. The method of claim 1, wherein predicting the quality characteristic of the product includes predicting a variation in an FTP pass rate associated with the quality of the product based on a production time of the product.
 18. The method of claim 1, wherein predicting the quality characteristic of the product includes: determining an FTP pass rate value for the product based on the identified feature and corresponding tolerance, the determined manufacturing and measuring characteristics, the determined assembly characteristic, and the determined desired quality characteristic.
 19. The method of claim 1, further including: determining category parts associated with the product, wherein the category parts, when assembled, form an assembly feature of the product.
 20. The method of claim 18, wherein the assembly feature is associated with a spatial distance between one or more assembled category parts in the product.
 21. A method of predicting a quality characteristic of a product to be manufactured by a manufacturer, comprising the steps of: determining at least three of a feature and tolerance characteristic associated with the product, a manufacturing characteristic associated with a manufacture of the product, a measurement characteristic associated with the manufacture of the product, assembly characteristic information associated with an assembly of the product, and desired quality characteristic information associated with the product; predicting the quality characteristic of the product based on the determined information; and providing the predicted quality characteristic of the product to the manufacturer.
 22. A method of determining a likelihood of failure of a product to be manufactured by a manufacturer, comprising the steps of: determining at least three of a feature and tolerance characteristic associated with the product, a manufacturing characteristic associated with a manufacture of the product, a measurement characteristic associated with the manufacture of the product, assembly characteristic information associated with an assembly of the product, and desired quality characteristic information associated with the product; obtaining a likelihood of failure based on the determined information; and providing the likelihood of failure to the manufacturer.
 23. The method of claim 22, further comprising the step of: determining a component of the product to be manufactured that is most likely to fail. 